We will compare some classifiers on the “Toxic” column.
Load libraries
library(tidyverse)package 㤼㸱tidyverse㤼㸲 was built under R version 4.0.5Registered S3 methods overwritten by 'dbplyr':
method from
print.tbl_lazy
print.tbl_sql
-- Attaching packages ---------------------------------------------------------------------------------------------------------------------------- tidyverse 1.3.1 --
v ggplot2 3.3.5 v purrr 0.3.4
v tibble 3.1.3 v dplyr 1.0.7
v tidyr 1.1.3 v stringr 1.4.0
v readr 2.0.1 v forcats 0.5.1
package 㤼㸱ggplot2㤼㸲 was built under R version 4.0.5package 㤼㸱tibble㤼㸲 was built under R version 4.0.5package 㤼㸱tidyr㤼㸲 was built under R version 4.0.5package 㤼㸱purrr㤼㸲 was built under R version 4.0.5package 㤼㸱dplyr㤼㸲 was built under R version 4.0.5package 㤼㸱stringr㤼㸲 was built under R version 4.0.5package 㤼㸱forcats㤼㸲 was built under R version 4.0.5-- Conflicts ------------------------------------------------------------------------------------------------------------------------------- tidyverse_conflicts() --
x dplyr::filter() masks stats::filter()
x dplyr::lag() masks stats::lag()library(tictoc)package 㤼㸱tictoc㤼㸲 was built under R version 4.0.5library(caret)package 㤼㸱caret㤼㸲 was built under R version 4.0.5Loading required package: lattice
Registered S3 method overwritten by 'data.table':
method from
print.data.table
Attaching package: 㤼㸱caret㤼㸲
The following object is masked from 㤼㸱package:purrr㤼㸲:
liftlibrary(e1071)package 㤼㸱e1071㤼㸲 was built under R version 4.0.5source("./parameters.R")# We open a relatively small Bag of Words in order to limit calculation time
fileName = "bow_tfidf__min_words_100_2grams_1000__sampling_balanced__cor_cut_1_from_1408_to_1408.csv"
df = read_csv(fileName, col_types=col_types_df)
df = df[,-c(2,3,5:9)]
dfdf_train = df[df[1] == 1,-1]
df_test = df[df[1] == 2,-1]
# Let's control the labels balance
writeLines("Toxic labels in the train set:")Toxic labels in the train set:table(df_train$df_toxic)
0 1
14688 14634 writeLines("\nToxic labels in the test set:")
Toxic labels in the test set:table(df_test$df_toxic)
0 1
5783 5841 tic("Training: ")
f <- glm(df_toxic ~ ., data=df_train, family = 'binomial')glm.fit: algorithm did not convergeglm.fit: fitted probabilities numerically 0 or 1 occurredtoc(log = TRUE)Training: : 1392.45 sec elapsedThis takes some time…
What’s in f ?
f
Call: glm(formula = df_toxic ~ ., family = "binomial", data = df_train)
Coefficients:
(Intercept) abil abl absolut abus academ accept access accord account accur
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accus achiev across act action activ actual add addit address admin
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administr admit advanc advertis advic afd afraid age agenda ago agre
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ahead aid air album alleg allow almost alon along alreadi also
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alter altern although alway american among amount anal and angri ani
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anim annoy anonym anoth answer anti anybodi anymor anyon anyth anyway
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anywher apolog appar appeal appear appli applic appreci approach appropri arab
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communist communiti compani compar complain complaint complet comput concept concern conclus
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damn dare data date day dead deal death debat decemb decid
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fuck fucker fuckin full fulli fun `function` funni furthermor futur game
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place plain plan play player pleas plenti plus point polic polici
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polit poor pop popul popular porn posit possibl post potenti power
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practic preced prefer present presid press pretend pretti prevent previous prick
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primari prior privat pro probabl problem process produc product profession program
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progress project promot proof propaganda proper propos protect prove provid public
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publish pull punish punk puppet pure purpos push pussi put qualifi
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qualiti question quick quit quot race racist rais random rape rate
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rather rational reach read reader real realiti realiz realli reason receiv
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recent recogn recommend record red redirect ref refer referenc reflect refrain
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refus regard regardless region regist regular relat relationship releas relev reliabl
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religi religion remain remark rememb remind remov renam `repeat` replac repli
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report repres reput request requir research resolv resourc respect respond
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[ reached getOption("max.print") -- omitted 409 entries ]
Degrees of Freedom: 29321 Total (i.e. Null); 27914 Residual
Null Deviance: 40650
Residual Deviance: 575200 AIC: 578000Let’s do the inference on the test set now.
# Split the test set
X_test = df_test[,-1]
Y_test = df_test$df_toxic
# Do the inference on the test set
tic("Inference: ")
Y_pred <- predict(f,X_test,type='response')prediction from a rank-deficient fit may be misleadingtoc(log = TRUE)Inference: : 7.35 sec elapsed# Add the predictions class and compare to the real values
predictions = as.data.frame(Y_pred)
predictions$predictions = round(predictions$Y_pred)
predictions$real = Y_test
# What does the confusion matrix gives us?
writeLines("\n")mat = confusionMatrix(as.factor(Y_test), as.factor(predictions$predictions))
matConfusion Matrix and Statistics
Reference
Prediction 0 1
0 2544 3239
1 245 5596
Accuracy : 0.7003
95% CI : (0.6919, 0.7086)
No Information Rate : 0.7601
P-Value [Acc > NIR] : 1
Kappa : 0.399
Mcnemar's Test P-Value : <2e-16
Sensitivity : 0.9122
Specificity : 0.6334
Pos Pred Value : 0.4399
Neg Pred Value : 0.9581
Prevalence : 0.2399
Detection Rate : 0.2189
Detection Prevalence : 0.4975
Balanced Accuracy : 0.7728
'Positive' Class : 0
print("END: all the notebook ran.")[1] "END: all the notebook ran."Sys.time()[1] "2021-08-23 22:42:03 CEST"writeLines(paste0("File: ", fileName))File: bow_tfidf__min_words_100_2grams_1000__sampling_balanced__cor_cut_1_from_1408_to_1408.csvwriteLines(paste0("Accuracy: ", mat$overall[1]))Accuracy: 0.700275292498279writeLines(paste0(tic.log(format = TRUE)[1][1]))Training: : 1392.45 sec elapsedwriteLines(paste0(tic.log(format = TRUE)[2][1]))Inference: : 7.35 sec elapsed